SurF applies the Time Rescaling Theorem as a learnable bijection to create a single generative model for forecasting irregular multivariate event streams that outperforms or matches baselines on six benchmarks.
arXiv preprint arXiv:2307.08097 , year=
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SNMPP builds a product-form neural influence kernel from a signed interaction network over event classes and a delay-aware monotonic temporal network to enable explicit discovery of inter-event relationships alongside strong prediction.
New 8.9M-event dataset from Pendle, Uniswap v3, Aave and Morpho plus UWM loss yields 56.41% average reduction in time-prediction error for TPP models while preserving event-type accuracy.
citing papers explorer
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SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
SurF applies the Time Rescaling Theorem as a learnable bijection to create a single generative model for forecasting irregular multivariate event streams that outperforms or matches baselines on six benchmarks.
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Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling
SNMPP builds a product-form neural influence kernel from a signed interaction network over event classes and a delay-aware monotonic temporal network to enable explicit discovery of inter-event relationships alongside strong prediction.
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Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols
New 8.9M-event dataset from Pendle, Uniswap v3, Aave and Morpho plus UWM loss yields 56.41% average reduction in time-prediction error for TPP models while preserving event-type accuracy.